Overview

Dataset statistics

 Dataset ADataset B
Number of variables88
Number of observations2122910415
Missing cells2325411349
Missing cells (%)13.7%13.6%
Duplicate rows770227
Duplicate rows (%)3.6%2.2%
Total size in memory1.5 MiB732.3 KiB
Average record size in memory72.0 B72.0 B

Variable types

 Dataset ADataset B
Categorical22
Numeric66

Alerts

Dataset ADataset B
Dataset has 770 (3.6%) duplicate rows Dataset has 227 (2.2%) duplicate rowsDuplicates
pressure is highly overall correlated with d_e and 2 other fieldspressure is highly overall correlated with d_e and 2 other fieldsHigh Correlation
d_e is highly overall correlated with pressure and 1 other fieldsd_e is highly overall correlated with pressure and 1 other fieldsHigh Correlation
d_h is highly overall correlated with pressure and 4 other fieldsd_h is highly overall correlated with pressure and 4 other fieldsHigh Correlation
length is highly overall correlated with d_h and 1 other fieldslength is highly overall correlated with d_hHigh Correlation
author is highly overall correlated with d_h and 1 other fieldsauthor is highly overall correlated with d_h and 1 other fieldsHigh Correlation
geometry is highly overall correlated with pressure and 3 other fieldsgeometry is highly overall correlated with pressure and 2 other fieldsHigh Correlation
author has 3403 (16.0%) missing values author has 1621 (15.6%) missing values Missing
geometry has 3713 (17.5%) missing values geometry has 1787 (17.2%) missing values Missing
pressure has 2986 (14.1%) missing values pressure has 1466 (14.1%) missing values Missing
mass_flux has 3227 (15.2%) missing values mass_flux has 1564 (15.0%) missing values Missing
d_e has 3641 (17.2%) missing values d_e has 1847 (17.7%) missing values Missing
d_h has 3127 (14.7%) missing values d_h has 1462 (14.0%) missing values Missing
length has 3157 (14.9%) missing values length has 1602 (15.4%) missing values Missing
Alert not present in geometry is highly imbalanced (50.0%) Imbalance

Reproduction

 Dataset ADataset B
Analysis started2023-05-31 22:43:57.1806552023-05-31 22:44:01.684720
Analysis finished2023-05-31 22:44:01.6785892023-05-31 22:44:06.041947
Duration4.5 seconds4.36 seconds
Software versionydata-profiling v0.0.dev0ydata-profiling v0.0.dev0
Download configurationconfig.jsonconfig.json

Variables

author
Categorical

 Dataset ADataset B
Distinct1010
Distinct (%)0.1%0.1%
Missing34031621
Missing (%)16.0%15.6%
Memory size331.7 KiB162.7 KiB
Thompson
11621 
Janssen
1846 
Weatherhead
1377 
Beus
 
1087
Peskov
 
729
Other values (5)
1166 
Thompson
5775 
Janssen
870 
Weatherhead
663 
Beus
 
517
Peskov
 
355
Other values (5)
614 

Length

 Dataset ADataset B
Max length1212
Median length88
Mean length7.89930447.9031158
Min length44

Characters and Unicode

 Dataset ADataset B
Total characters14081369500
Distinct characters2727
Distinct categories22 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Dataset ADataset B
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Dataset ADataset B
1st rowThompsonPeskov
2nd rowThompsonThompson
3rd rowThompsonThompson
4th rowBeusBeus
5th rowThompsonWeatherhead

Common Values

ValueCountFrequency (%)
Thompson 11621
54.7%
Janssen 1846
 
8.7%
Weatherhead 1377
 
6.5%
Beus 1087
 
5.1%
Peskov 729
 
3.4%
Williams 567
 
2.7%
Richenderfer 371
 
1.7%
Mortimore 130
 
0.6%
Kossolapov 70
 
0.3%
Inasaka 28
 
0.1%
(Missing) 3403
 
16.0%
ValueCountFrequency (%)
Thompson 5775
55.4%
Janssen 870
 
8.4%
Weatherhead 663
 
6.4%
Beus 517
 
5.0%
Peskov 355
 
3.4%
Williams 324
 
3.1%
Richenderfer 174
 
1.7%
Mortimore 67
 
0.6%
Kossolapov 31
 
0.3%
Inasaka 18
 
0.2%
(Missing) 1621
 
15.6%

Length

2023-05-31T18:44:06.137340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Dataset A

2023-05-31T18:44:06.267256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:06.399824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
thompson 11621
65.2%
janssen 1846
 
10.4%
weatherhead 1377
 
7.7%
beus 1087
 
6.1%
peskov 729
 
4.1%
williams 567
 
3.2%
richenderfer 371
 
2.1%
mortimore 130
 
0.7%
kossolapov 70
 
0.4%
inasaka 28
 
0.2%
ValueCountFrequency (%)
thompson 5775
65.7%
janssen 870
 
9.9%
weatherhead 663
 
7.5%
beus 517
 
5.9%
peskov 355
 
4.0%
williams 324
 
3.7%
richenderfer 174
 
2.0%
mortimore 67
 
0.8%
kossolapov 31
 
0.4%
inasaka 18
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 24441
17.4%
s 17864
12.7%
n 15712
11.2%
h 14746
10.5%
m 12318
8.7%
p 11691
8.3%
T 11621
8.3%
e 9036
 
6.4%
a 5321
 
3.8%
r 2379
 
1.7%
Other values (17) 15684
11.1%
ValueCountFrequency (%)
o 12132
17.5%
s 8791
12.6%
n 7707
11.1%
h 7275
10.5%
m 6166
8.9%
p 5806
8.4%
T 5775
8.3%
e 4320
 
6.2%
a 2605
 
3.7%
r 1145
 
1.6%
Other values (17) 7778
11.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 122987
87.3%
Uppercase Letter 17826
 
12.7%
ValueCountFrequency (%)
Lowercase Letter 60706
87.3%
Uppercase Letter 8794
 
12.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 24441
19.9%
s 17864
14.5%
n 15712
12.8%
h 14746
12.0%
m 12318
10.0%
p 11691
9.5%
e 9036
 
7.3%
a 5321
 
4.3%
r 2379
 
1.9%
d 1748
 
1.4%
Other values (8) 7731
 
6.3%
ValueCountFrequency (%)
o 12132
20.0%
s 8791
14.5%
n 7707
12.7%
h 7275
12.0%
m 6166
10.2%
p 5806
9.6%
e 4320
 
7.1%
a 2605
 
4.3%
r 1145
 
1.9%
i 889
 
1.5%
Other values (8) 3870
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
T 11621
65.2%
W 1944
 
10.9%
J 1846
 
10.4%
B 1087
 
6.1%
P 729
 
4.1%
R 371
 
2.1%
M 130
 
0.7%
K 70
 
0.4%
I 28
 
0.2%
ValueCountFrequency (%)
T 5775
65.7%
W 987
 
11.2%
J 870
 
9.9%
B 517
 
5.9%
P 355
 
4.0%
R 174
 
2.0%
M 67
 
0.8%
K 31
 
0.4%
I 18
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 140813
100.0%
ValueCountFrequency (%)
Latin 69500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 24441
17.4%
s 17864
12.7%
n 15712
11.2%
h 14746
10.5%
m 12318
8.7%
p 11691
8.3%
T 11621
8.3%
e 9036
 
6.4%
a 5321
 
3.8%
r 2379
 
1.7%
Other values (17) 15684
11.1%
ValueCountFrequency (%)
o 12132
17.5%
s 8791
12.6%
n 7707
11.1%
h 7275
10.5%
m 6166
8.9%
p 5806
8.4%
T 5775
8.3%
e 4320
 
6.2%
a 2605
 
3.7%
r 1145
 
1.6%
Other values (17) 7778
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140813
100.0%
ValueCountFrequency (%)
ASCII 69500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 24441
17.4%
s 17864
12.7%
n 15712
11.2%
h 14746
10.5%
m 12318
8.7%
p 11691
8.3%
T 11621
8.3%
e 9036
 
6.4%
a 5321
 
3.8%
r 2379
 
1.7%
Other values (17) 15684
11.1%
ValueCountFrequency (%)
o 12132
17.5%
s 8791
12.6%
n 7707
11.1%
h 7275
10.5%
m 6166
8.9%
p 5806
8.4%
T 5775
8.3%
e 4320
 
6.2%
a 2605
 
3.7%
r 1145
 
1.6%
Other values (17) 7778
11.2%

geometry
Categorical

 Dataset ADataset B
Distinct33
Distinct (%)< 0.1%< 0.1%
Missing37131787
Missing (%)17.5%17.2%
Memory size331.7 KiB162.7 KiB
tube
14121 
annulus
2971 
plate
 
424
tube
7024 
annulus
1410 
plate
 
194

Length

 Dataset ADataset B
Max length77
Median length44
Mean length4.53305554.5127492
Min length44

Characters and Unicode

 Dataset ADataset B
Total characters7940138936
Distinct characters99
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Dataset ADataset B
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Dataset ADataset B
1st rowtubetube
2nd rowtubetube
3rd rowannulustube
4th rowtubeannulus
5th rowtubetube

Common Values

ValueCountFrequency (%)
tube 14121
66.5%
annulus 2971
 
14.0%
plate 424
 
2.0%
(Missing) 3713
 
17.5%
ValueCountFrequency (%)
tube 7024
67.4%
annulus 1410
 
13.5%
plate 194
 
1.9%
(Missing) 1787
 
17.2%

Length

2023-05-31T18:44:06.508043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Dataset A

2023-05-31T18:44:06.604810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:06.699069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
tube 14121
80.6%
annulus 2971
 
17.0%
plate 424
 
2.4%
ValueCountFrequency (%)
tube 7024
81.4%
annulus 1410
 
16.3%
plate 194
 
2.2%

Most occurring characters

ValueCountFrequency (%)
u 20063
25.3%
t 14545
18.3%
e 14545
18.3%
b 14121
17.8%
n 5942
 
7.5%
a 3395
 
4.3%
l 3395
 
4.3%
s 2971
 
3.7%
p 424
 
0.5%
ValueCountFrequency (%)
u 9844
25.3%
t 7218
18.5%
e 7218
18.5%
b 7024
18.0%
n 2820
 
7.2%
a 1604
 
4.1%
l 1604
 
4.1%
s 1410
 
3.6%
p 194
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79401
100.0%
ValueCountFrequency (%)
Lowercase Letter 38936
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 20063
25.3%
t 14545
18.3%
e 14545
18.3%
b 14121
17.8%
n 5942
 
7.5%
a 3395
 
4.3%
l 3395
 
4.3%
s 2971
 
3.7%
p 424
 
0.5%
ValueCountFrequency (%)
u 9844
25.3%
t 7218
18.5%
e 7218
18.5%
b 7024
18.0%
n 2820
 
7.2%
a 1604
 
4.1%
l 1604
 
4.1%
s 1410
 
3.6%
p 194
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 79401
100.0%
ValueCountFrequency (%)
Latin 38936
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 20063
25.3%
t 14545
18.3%
e 14545
18.3%
b 14121
17.8%
n 5942
 
7.5%
a 3395
 
4.3%
l 3395
 
4.3%
s 2971
 
3.7%
p 424
 
0.5%
ValueCountFrequency (%)
u 9844
25.3%
t 7218
18.5%
e 7218
18.5%
b 7024
18.0%
n 2820
 
7.2%
a 1604
 
4.1%
l 1604
 
4.1%
s 1410
 
3.6%
p 194
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79401
100.0%
ValueCountFrequency (%)
ASCII 38936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 20063
25.3%
t 14545
18.3%
e 14545
18.3%
b 14121
17.8%
n 5942
 
7.5%
a 3395
 
4.3%
l 3395
 
4.3%
s 2971
 
3.7%
p 424
 
0.5%
ValueCountFrequency (%)
u 9844
25.3%
t 7218
18.5%
e 7218
18.5%
b 7024
18.0%
n 2820
 
7.2%
a 1604
 
4.1%
l 1604
 
4.1%
s 1410
 
3.6%
p 194
 
0.5%

pressure
Real number (ℝ)

 Dataset ADataset B
Distinct140118
Distinct (%)0.8%1.3%
Missing29861466
Missing (%)14.1%14.1%
Infinite00
Infinite (%)0.0%0.0%
Mean10.63506610.65233
 Dataset ADataset B
Minimum0.10.1
Maximum20.6820.68
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size331.7 KiB162.7 KiB
2023-05-31T18:44:06.820723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

 Dataset ADataset B
Minimum0.10.1
5-th percentile3.453.45
Q16.896.89
median11.0311.07
Q313.7913.79
95-th percentile17.2417.24
Maximum20.6820.68
Range20.5820.58
Interquartile range (IQR)6.96.9

Descriptive statistics

 Dataset ADataset B
Standard deviation4.33294334.3354087
Coefficient of variation (CV)0.407420460.40699159
Kurtosis-0.55906368-0.55938547
Mean10.63506610.65233
Median Absolute Deviation (MAD)2.762.8
Skewness-0.34582278-0.35270669
Sum194015.595327.7
Variance18.77439718.795768
MonotonicityNot monotonicNot monotonic
2023-05-31T18:44:06.968511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.79 6179
29.1%
6.89 3145
14.8%
15.51 743
 
3.5%
10.34 719
 
3.4%
11.03 598
 
2.8%
3.45 409
 
1.9%
6.86 405
 
1.9%
12.07 355
 
1.7%
17.24 348
 
1.6%
18.96 332
 
1.6%
Other values (130) 5010
23.6%
(Missing) 2986
14.1%
ValueCountFrequency (%)
13.79 3047
29.3%
6.89 1556
14.9%
15.51 374
 
3.6%
10.34 345
 
3.3%
11.03 274
 
2.6%
3.45 230
 
2.2%
6.86 196
 
1.9%
0.1 178
 
1.7%
12.07 175
 
1.7%
18.96 167
 
1.6%
Other values (108) 2407
23.1%
(Missing) 1466
14.1%
ValueCountFrequency (%)
0.1 315
1.5%
0.2 83
 
0.4%
0.3 1
 
< 0.1%
0.31 6
 
< 0.1%
0.33 5
 
< 0.1%
0.34 2
 
< 0.1%
0.36 1
 
< 0.1%
0.39 6
 
< 0.1%
0.51 98
 
0.5%
0.62 6
 
< 0.1%
ValueCountFrequency (%)
0.1 178
1.7%
0.2 34
 
0.3%
0.3 1
 
< 0.1%
0.31 3
 
< 0.1%
0.33 3
 
< 0.1%
0.39 7
 
0.1%
0.51 38
 
0.4%
0.62 3
 
< 0.1%
0.64 5
 
< 0.1%
0.91 2
 
< 0.1%
ValueCountFrequency (%)
0.1 178
0.8%
0.2 34
 
0.2%
0.3 1
 
< 0.1%
0.31 3
 
< 0.1%
0.33 3
 
< 0.1%
0.39 7
 
< 0.1%
0.51 38
 
0.2%
0.62 3
 
< 0.1%
0.64 5
 
< 0.1%
0.91 2
 
< 0.1%
ValueCountFrequency (%)
0.1 315
3.0%
0.2 83
 
0.8%
0.3 1
 
< 0.1%
0.31 6
 
0.1%
0.33 5
 
< 0.1%
0.34 2
 
< 0.1%
0.36 1
 
< 0.1%
0.39 6
 
0.1%
0.51 98
 
0.9%
0.62 6
 
0.1%

mass_flux
Real number (ℝ)

 Dataset ADataset B
Distinct689619
Distinct (%)3.8%7.0%
Missing32271564
Missing (%)15.2%15.0%
Infinite00
Infinite (%)0.0%0.0%
Mean3070.48783062.9736
 Dataset ADataset B
Minimum00
Maximum79757975
Zeros54
Zeros (%)< 0.1%< 0.1%
Negative00
Negative (%)0.0%0.0%
Memory size331.7 KiB162.7 KiB
2023-05-31T18:44:07.147799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

 Dataset ADataset B
Minimum00
5-th percentile833783
Q115051519
median27302740
Q340694069
95-th percentile63476225.5
Maximum79757975
Range79757975
Interquartile range (IQR)25642550

Descriptive statistics

 Dataset ADataset B
Standard deviation1784.87311761.0661
Coefficient of variation (CV)0.581299540.57495309
Kurtosis-0.15296149-0.18679136
Mean3070.48783062.9736
Median Absolute Deviation (MAD)13261316
Skewness0.721383930.69296372
Sum5527492127110379
Variance3185772.13101353.8
MonotonicityNot monotonicNot monotonic
2023-05-31T18:44:07.336369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4069 623
 
2.9%
1519 437
 
2.1%
1356 411
 
1.9%
2034 341
 
1.6%
1000 291
 
1.4%
1383 181
 
0.9%
3811 166
 
0.8%
2292 164
 
0.8%
1533 160
 
0.8%
4055 158
 
0.7%
Other values (679) 15070
71.0%
(Missing) 3227
 
15.2%
ValueCountFrequency (%)
4069 340
 
3.3%
1356 204
 
2.0%
1519 197
 
1.9%
2034 192
 
1.8%
1000 127
 
1.2%
4096 101
 
1.0%
1383 85
 
0.8%
2292 83
 
0.8%
3784 83
 
0.8%
3838 81
 
0.8%
Other values (609) 7358
70.6%
(Missing) 1564
 
15.0%
ValueCountFrequency (%)
0 5
 
< 0.1%
4 4
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
310 10
< 0.1%
332 2
 
< 0.1%
336 9
< 0.1%
339 8
< 0.1%
340 19
0.1%
ValueCountFrequency (%)
0 4
< 0.1%
8 1
 
< 0.1%
82 1
 
< 0.1%
148 1
 
< 0.1%
310 7
0.1%
332 2
 
< 0.1%
336 6
0.1%
339 2
 
< 0.1%
340 6
0.1%
346 2
 
< 0.1%
ValueCountFrequency (%)
0 4
< 0.1%
8 1
 
< 0.1%
82 1
 
< 0.1%
148 1
 
< 0.1%
310 7
< 0.1%
332 2
 
< 0.1%
336 6
< 0.1%
339 2
 
< 0.1%
340 6
< 0.1%
346 2
 
< 0.1%
ValueCountFrequency (%)
0 5
 
< 0.1%
4 4
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
310 10
0.1%
332 2
 
< 0.1%
336 9
0.1%
339 8
0.1%
340 19
0.2%

d_e
Real number (ℝ)

 Dataset ADataset B
Distinct4137
Distinct (%)0.2%0.4%
Missing36411847
Missing (%)17.2%17.7%
Infinite00
Infinite (%)0.0%0.0%
Mean8.58930528.7112628
 Dataset ADataset B
Minimum11
Maximum37.537.5
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size331.7 KiB162.7 KiB
2023-05-31T18:44:07.501064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

 Dataset ADataset B
Minimum11
5-th percentile1.91.9
Q14.75
median7.88.5
Q310.810.8
95-th percentile1515
Maximum37.537.5
Range36.536.5
Interquartile range (IQR)6.15.8

Descriptive statistics

 Dataset ADataset B
Standard deviation5.13220715.2931496
Coefficient of variation (CV)0.597511320.60762138
Kurtosis9.13737138.9280928
Mean8.58930528.7112628
Median Absolute Deviation (MAD)32.9
Skewness2.17072512.1929428
Sum151068.774638.1
Variance26.3395528.017432
MonotonicityNot monotonicNot monotonic
2023-05-31T18:44:08.035533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
10.3 1790
 
8.4%
1.9 1692
 
8.0%
10.8 1686
 
7.9%
4.7 1637
 
7.7%
7.7 1552
 
7.3%
5.6 1417
 
6.7%
12.7 963
 
4.5%
7.8 872
 
4.1%
10 740
 
3.5%
9.5 564
 
2.7%
Other values (31) 4675
22.0%
(Missing) 3641
17.2%
ValueCountFrequency (%)
10.3 894
 
8.6%
10.8 826
 
7.9%
1.9 807
 
7.7%
4.7 795
 
7.6%
7.7 742
 
7.1%
5.6 691
 
6.6%
12.7 470
 
4.5%
7.8 411
 
3.9%
10 340
 
3.3%
9.5 317
 
3.0%
Other values (27) 2275
21.8%
(Missing) 1847
17.7%
ValueCountFrequency (%)
1 93
 
0.4%
1.1 12
 
0.1%
1.7 6
 
< 0.1%
1.9 1692
8.0%
3 325
 
1.5%
3.6 188
 
0.9%
4.6 457
 
2.2%
4.7 1637
7.7%
5 130
 
0.6%
5.6 1417
6.7%
ValueCountFrequency (%)
1 60
 
0.6%
1.1 6
 
0.1%
1.7 2
 
< 0.1%
1.9 807
7.7%
3 158
 
1.5%
3.6 93
 
0.9%
4.6 201
 
1.9%
4.7 795
7.6%
5 59
 
0.6%
5.6 691
6.6%
ValueCountFrequency (%)
1 60
 
0.3%
1.1 6
 
< 0.1%
1.7 2
 
< 0.1%
1.9 807
3.8%
3 158
 
0.7%
3.6 93
 
0.4%
4.6 201
 
0.9%
4.7 795
3.7%
5 59
 
0.3%
5.6 691
3.3%
ValueCountFrequency (%)
1 93
 
0.9%
1.1 12
 
0.1%
1.7 6
 
0.1%
1.9 1692
16.2%
3 325
 
3.1%
3.6 188
 
1.8%
4.6 457
 
4.4%
4.7 1637
15.7%
5 130
 
1.2%
5.6 1417
13.6%

d_h
Real number (ℝ)

 Dataset ADataset B
Distinct4744
Distinct (%)0.3%0.5%
Missing31271462
Missing (%)14.7%14.0%
Infinite00
Infinite (%)0.0%0.0%
Mean14.21544614.091198
 Dataset ADataset B
Minimum11
Maximum120120
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size331.7 KiB162.7 KiB
2023-05-31T18:44:08.191182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

 Dataset ADataset B
Minimum11
5-th percentile1.91.9
Q15.65.6
median1010
Q311.511.5
95-th percentile42.342.3
Maximum120120
Range119119
Interquartile range (IQR)5.95.9

Descriptive statistics

 Dataset ADataset B
Standard deviation19.91359419.686604
Coefficient of variation (CV)1.4008421.3970852
Kurtosis18.28338718.705588
Mean14.21544614.091198
Median Absolute Deviation (MAD)4.33.3
Skewness4.11886114.1555643
Sum257328126158.5
Variance396.55123387.56239
MonotonicityNot monotonicNot monotonic
2023-05-31T18:44:08.348501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
10.3 1869
 
8.8%
10.8 1735
 
8.2%
1.9 1727
 
8.1%
4.7 1701
 
8.0%
7.7 1587
 
7.5%
15.2 1061
 
5.0%
7.8 885
 
4.2%
10 753
 
3.5%
42.3 583
 
2.7%
9.5 575
 
2.7%
Other values (37) 5626
26.5%
(Missing) 3127
14.7%
ValueCountFrequency (%)
10.3 923
 
8.9%
10.8 863
 
8.3%
4.7 853
 
8.2%
1.9 842
 
8.1%
7.7 795
 
7.6%
15.2 517
 
5.0%
7.8 428
 
4.1%
10 362
 
3.5%
9.5 329
 
3.2%
42.3 281
 
2.7%
Other values (34) 2760
26.5%
(Missing) 1462
14.0%
ValueCountFrequency (%)
1 100
 
0.5%
1.1 11
 
0.1%
1.7 7
 
< 0.1%
1.9 1727
8.1%
3 326
 
1.5%
3.6 192
 
0.9%
4.6 397
 
1.9%
4.7 1701
8.0%
5.6 377
 
1.8%
5.7 283
 
1.3%
ValueCountFrequency (%)
1 65
 
0.6%
1.1 6
 
0.1%
1.7 2
 
< 0.1%
1.9 842
8.1%
3 166
 
1.6%
3.6 93
 
0.9%
4.6 207
 
2.0%
4.7 853
8.2%
5.6 193
 
1.9%
5.7 117
 
1.1%
ValueCountFrequency (%)
1 65
 
0.3%
1.1 6
 
< 0.1%
1.7 2
 
< 0.1%
1.9 842
4.0%
3 166
 
0.8%
3.6 93
 
0.4%
4.6 207
 
1.0%
4.7 853
4.0%
5.6 193
 
0.9%
5.7 117
 
0.6%
ValueCountFrequency (%)
1 100
 
1.0%
1.1 11
 
0.1%
1.7 7
 
0.1%
1.9 1727
16.6%
3 326
 
3.1%
3.6 192
 
1.8%
4.6 397
 
3.8%
4.7 1701
16.3%
5.6 377
 
3.6%
5.7 283
 
2.7%

length
Real number (ℝ)

 Dataset ADataset B
Distinct6560
Distinct (%)0.4%0.7%
Missing31571602
Missing (%)14.9%15.4%
Infinite00
Infinite (%)0.0%0.0%
Mean830.56496837.95484
 Dataset ADataset B
Minimum1010
Maximum30483048
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size331.7 KiB162.7 KiB
2023-05-31T18:44:08.516802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

 Dataset ADataset B
Minimum1010
5-th percentile100100
Q1318318
median610610
Q3914914
95-th percentile21342134
Maximum30483048
Range30383038
Interquartile range (IQR)596596

Descriptive statistics

 Dataset ADataset B
Standard deviation671.14217674.67667
Coefficient of variation (CV)0.8080550.80514681
Kurtosis-0.10396928-0.12321172
Mean830.56496837.95484
Median Absolute Deviation (MAD)292292
Skewness1.03174811.0169489
Sum150099707384896
Variance450431.81455188.6
MonotonicityNot monotonicNot monotonic
2023-05-31T18:44:08.696466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
457 2140
 
10.1%
762 1575
 
7.4%
318 1456
 
6.9%
2134 1252
 
5.9%
152 1227
 
5.8%
432 1013
 
4.8%
591 897
 
4.2%
1778 792
 
3.7%
914 673
 
3.2%
864 617
 
2.9%
Other values (55) 6430
30.3%
(Missing) 3157
14.9%
ValueCountFrequency (%)
457 1040
 
10.0%
762 789
 
7.6%
318 698
 
6.7%
2134 581
 
5.6%
152 573
 
5.5%
432 499
 
4.8%
591 405
 
3.9%
1778 353
 
3.4%
914 343
 
3.3%
1836 331
 
3.2%
Other values (50) 3201
30.7%
(Missing) 1602
15.4%
ValueCountFrequency (%)
10 479
2.3%
12 1
 
< 0.1%
20 1
 
< 0.1%
25 34
 
0.2%
35 75
 
0.4%
38 33
 
0.2%
43 5
 
< 0.1%
51 42
 
0.2%
64 15
 
0.1%
76 187
 
0.9%
ValueCountFrequency (%)
10 230
2.2%
25 22
 
0.2%
35 25
 
0.2%
38 23
 
0.2%
43 2
 
< 0.1%
51 23
 
0.2%
64 7
 
0.1%
76 95
0.9%
96 1
 
< 0.1%
100 21
 
0.2%
ValueCountFrequency (%)
10 230
1.1%
25 22
 
0.1%
35 25
 
0.1%
38 23
 
0.1%
43 2
 
< 0.1%
51 23
 
0.1%
64 7
 
< 0.1%
76 95
0.4%
96 1
 
< 0.1%
100 21
 
0.1%
ValueCountFrequency (%)
10 479
4.6%
12 1
 
< 0.1%
20 1
 
< 0.1%
25 34
 
0.3%
35 75
 
0.7%
38 33
 
0.3%
43 5
 
< 0.1%
51 42
 
0.4%
64 15
 
0.1%
76 187
 
1.8%

chf_exp
Real number (ℝ)

 Dataset ADataset B
Distinct109109
Distinct (%)0.5%1.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean3.8091293.7722324
 Dataset ADataset B
Minimum0.80.8
Maximum19.319.3
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size331.7 KiB162.7 KiB
2023-05-31T18:44:08.842078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

 Dataset ADataset B
Minimum0.80.8
5-th percentile1.61.6
Q12.42.4
median3.43.4
Q34.74.6
95-th percentile7.57.5
Maximum19.319.3
Range18.518.5
Interquartile range (IQR)2.32.2

Descriptive statistics

 Dataset ADataset B
Standard deviation1.9880091.9756402
Coefficient of variation (CV)0.521906440.52373238
Kurtosis6.05492465.7642107
Mean3.8091293.7722324
Median Absolute Deviation (MAD)1.11.1
Skewness1.84395241.8129017
Sum8086439287.8
Variance3.95217973.9031544
MonotonicityNot monotonicNot monotonic
2023-05-31T18:44:08.984983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.3 861
 
4.1%
2.2 752
 
3.5%
2.5 736
 
3.5%
2.1 667
 
3.1%
3.6 664
 
3.1%
2.6 639
 
3.0%
3.2 614
 
2.9%
2.7 610
 
2.9%
2 606
 
2.9%
1.8 576
 
2.7%
Other values (99) 14504
68.3%
ValueCountFrequency (%)
2.5 408
 
3.9%
2.3 399
 
3.8%
2.2 382
 
3.7%
3.6 322
 
3.1%
2.7 320
 
3.1%
2.1 313
 
3.0%
2.6 311
 
3.0%
1.8 296
 
2.8%
2 294
 
2.8%
3.2 292
 
2.8%
Other values (99) 7078
68.0%
ValueCountFrequency (%)
0.8 12
 
0.1%
0.9 57
 
0.3%
1 88
 
0.4%
1.1 140
0.7%
1.2 107
 
0.5%
1.3 140
0.7%
1.4 173
0.8%
1.5 243
1.1%
1.6 272
1.3%
1.7 154
0.7%
ValueCountFrequency (%)
0.8 4
 
< 0.1%
0.9 38
 
0.4%
1 48
 
0.5%
1.1 66
 
0.6%
1.2 54
 
0.5%
1.3 78
0.7%
1.4 105
1.0%
1.5 100
1.0%
1.6 170
1.6%
1.7 89
0.9%
ValueCountFrequency (%)
0.8 4
 
< 0.1%
0.9 38
 
0.2%
1 48
 
0.2%
1.1 66
 
0.3%
1.2 54
 
0.3%
1.3 78
0.4%
1.4 105
0.5%
1.5 100
0.5%
1.6 170
0.8%
1.7 89
0.4%
ValueCountFrequency (%)
0.8 12
 
0.1%
0.9 57
 
0.5%
1 88
 
0.8%
1.1 140
1.3%
1.2 107
 
1.0%
1.3 140
1.3%
1.4 173
1.7%
1.5 243
2.3%
1.6 272
2.6%
1.7 154
1.5%

Interactions

Dataset A

2023-05-31T18:44:00.591233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:05.107601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:57.528748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:01.887600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:58.106037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:02.738271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:58.845589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:03.375596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:59.460877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:03.968599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:44:00.030022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:04.532621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:44:00.701468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:05.191448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:57.620231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:02.283669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:58.230582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:02.843961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:58.924361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:03.473177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:59.545397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:04.062937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:44:00.119692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:04.630827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:44:00.811083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:05.287267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:57.745286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:02.370739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:58.347914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:02.976204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:59.042034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:03.576270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:59.659204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:04.154464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:44:00.208531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:04.724336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:44:00.913426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:05.387714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:57.842182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:02.453439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:58.468740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:03.089469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:59.147105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:03.674093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:59.763715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:04.246008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:44:00.312845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:04.823783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:44:01.018884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:05.484801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:57.923229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:02.539332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:58.603487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:03.180426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:59.239615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:03.759462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:59.845497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:04.335821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:44:00.404666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:04.909285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:44:01.122427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:05.582030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:58.007245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:02.626130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:58.732928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:03.268312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:59.358833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:03.848966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:43:59.926060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:04.427034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

2023-05-31T18:44:00.498177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:05.008199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

Dataset A

2023-05-31T18:44:09.090310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset B

2023-05-31T18:44:09.195259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Dataset A

pressuremass_fluxd_ed_hlengthchf_expauthorgeometry
pressure1.000-0.227-0.579-0.544-0.152-0.3140.3700.588
mass_flux-0.2271.0000.006-0.0790.0590.3520.1910.219
d_e-0.5790.0061.0000.8060.4070.0660.3420.465
d_h-0.544-0.0790.8061.0000.625-0.0860.6310.902
length-0.1520.0590.4070.6251.000-0.2830.4310.511
chf_exp-0.3140.3520.066-0.086-0.2831.0000.1520.176
author0.3700.1910.3420.6310.4310.1521.0000.962
geometry0.5880.2190.4650.9020.5110.1760.9621.000

Dataset B

pressuremass_fluxd_ed_hlengthchf_expauthorgeometry
pressure1.000-0.245-0.575-0.529-0.132-0.3380.3660.568
mass_flux-0.2451.0000.012-0.0660.0800.3340.1900.214
d_e-0.5750.0121.0000.8200.4090.0560.3700.459
d_h-0.529-0.0660.8201.0000.621-0.0790.7030.896
length-0.1320.0800.4090.6211.000-0.2850.4260.491
chf_exp-0.3380.3340.056-0.079-0.2851.0000.1490.160
author0.3660.1900.3700.7030.4260.1491.0000.961
geometry0.5680.2140.4590.8960.4910.1600.9611.000

Missing values

Dataset A

2023-05-31T18:44:01.261486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.

Dataset B

2023-05-31T18:44:05.730583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.

Dataset A

2023-05-31T18:44:01.408999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset B

2023-05-31T18:44:05.846530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset A

2023-05-31T18:44:01.565570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Dataset B

2023-05-31T18:44:05.967831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Dataset A

authorgeometrypressuremass_fluxd_ed_hlengthchf_exp
id
0Thompsontube7.003770.0NaN10.8432.03.6
1ThompsontubeNaN6049.010.310.3762.06.2
2ThompsonNaN13.792034.07.77.7457.02.5
3Beusannulus13.793679.05.615.22134.03.0
5NaNNaN17.243648.0NaN1.9696.03.6
6ThompsonNaN6.89549.012.812.81930.02.6
8NaNtube12.074042.0NaNNaN152.05.6
9Peskovtube12.001617.010.010.0520.02.2
11Janssenannulus4.131519.012.7NaN1778.04.6
13Peskovtube12.002794.010.0NaN1650.02.9

Dataset B

authorgeometrypressuremass_fluxd_ed_hlengthchf_exp
id
4NaNtube13.79686.011.111.1457.02.8
7Peskovtube18.00750.010.010.01650.02.2
10ThompsontubeNaNNaN1.91.9152.03.2
12ThompsonNaN6.897500.0NaN12.81930.04.8
23Beusannulus15.511355.05.615.22134.02.1
27WeatherheadNaN13.79NaN11.111.1457.03.5
34Thompsontube13.791275.0NaN7.8591.02.4
36Thompsontube13.791655.07.77.7457.03.8
39Thompsontube13.795588.05.75.7625.03.3
43Thompsontube18.962699.01.91.9696.02.2

Dataset A

authorgeometrypressuremass_fluxd_ed_hlengthchf_exp
id
31627Thompsontube0.643282.03.03.0100.07.1
31628Thompsontube13.791302.04.74.7NaN2.5
31630Janssenannulus6.892807.06.4NaN914.04.5
31631Thompsontube3.86NaN10.810.8432.04.1
31635Thompsontube17.242984.01.91.9152.03.9
31636NaNNaN12.07NaNNaN1.9152.05.4
31638ThompsontubeNaN3648.04.74.7318.09.0
31639ThompsonNaNNaN1736.0NaN7.8591.02.3
31641ThompsonNaN18.27658.03.03.0150.02.3
31643NaNtube6.897568.012.812.81930.03.3

Dataset B

authorgeometrypressuremass_fluxd_ed_hlengthchf_exp
id
31620ThompsonNaN15.513024.01.91.9NaN6.4
31621Thompsontube6.864062.010.8NaN1727.04.2
31625ThompsontubeNaN3637.04.64.6229.012.8
31629ThompsonNaN13.794964.0NaN4.7318.03.9
31632Thompsontube18.27833.0NaNNaN150.04.1
31633Thompsontube11.03NaN11.511.5NaN2.0
31634Richenderferplate1.012000.015.0120.010.06.2
31637Weatherheadtube13.79688.0NaN11.1457.02.3
31640NaNNaN13.79NaN4.74.7NaN3.9
31642Thompsontube6.893825.023.623.61972.03.7

Duplicate rows

Dataset A

authorgeometrypressuremass_fluxd_ed_hlengthchf_exp# duplicates
356Thompsontube13.791233.07.87.8591.02.39
531Thompsontube13.79NaN7.87.8591.02.69
529Thompsontube13.79NaN7.87.8591.02.38
514Thompsontube13.79NaN7.77.7457.02.37
520Thompsontube13.79NaN7.77.7457.03.66
449Thompsontube13.793648.04.74.7318.03.25
527Thompsontube13.79NaN7.87.8591.02.05
685Weatherheadtube13.79NaN7.77.7457.02.65
4Beusannulus11.031355.05.615.22134.02.14
27Beusannulus13.79NaN5.615.22134.02.14

Dataset B

authorgeometrypressuremass_fluxd_ed_hlengthchf_exp# duplicates
118Thompsontube13.791465.07.87.8591.02.54
164Thompsontube13.79NaN7.87.8591.02.94
11Beusannulus15.51NaN5.615.22134.01.63
16BeusannulusNaNNaN5.615.22134.02.13
29Janssenannulus6.89NaN8.522.32743.02.23
46Thompsontube3.455696.010.310.3762.04.43
76Thompsontube6.89NaN37.537.51953.02.03
115Thompsontube13.791370.07.77.7457.04.53
124Thompsontube13.791736.07.87.8591.02.13
144Thompsontube13.79NaN4.74.7318.02.73